We consider a UAV-aided cellular network with heterogeneous Radio Access Technologies (RATs) as the 6G network model. There are \(N_h\)  number of hgNBs, consisting of \(N_u\)  number of UAVs, denoted by \(u_{k}\) , for (\(1 \le k \le N_u\) ), along with \(N_f\)  number of FAPs, denoted by \(f_i\) , for (\(1 \le i \le N_f\) ), serving requests of \(N_g\)  number of UEs, denoted by \(U_j\) , for (\(1 \le j \le N_g\) ). The hgNBs are equipped with a limited cache size, denoted by \(K\) . As shown in Fig. REF , FAPs are independently and randomly distributed in the environment following a Poisson Point Process (PPP) [1]}. We also consider a Gaussian mixture distribution for UEs, leading to a dense population in some environments. Due to the movement of UEs, the population is changed over time, therefore, the location of UAVs are determined by the \(K\) -means clustering algorithm [2]}, where UAVs remain hovering at their locations while serving a request for data delivery [3]}. A Software Defined Network (SDN) controller is used to manage the aerial and terrestrial connections and control the link quality and topology of UAVs [3]}. We denote a library of content \(\mathcal {C} = \lbrace c_1, \ldots , c_{N_c} \rbrace \) , where \(N_c=|\mathcal {C}|\)  is the cardinality of contents in the network. For simplicity, it is assumed that the size of all contents \(c_l\) , for (\(1 \le l \le N_c\) ), are the same [3]}, and UEs request at most one content in each time slot.
